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Issue No. 08 - Aug. (2013 vol. 25)
ISSN: 1041-4347
pp: 1787-1800
Jinhan Kim , Pohang University of Science and Technology (POSTECH), Pohang
Seung-won Hwang , Pohang University of Science and Technology (POSTECH), Pohang
Long Jiang , Microsoft Research Asia, Beijing
Young-In Song , Microsoft Research Asia, Beijing
Ming Zhou , Microsoft Research Asia, Beijing
This paper addresses the problem of mining named entity translations from comparable corpora, specifically, mining English and Chinese named entity translation. We first observe that existing approaches use one or more of the following named entity similarity metrics: entity, entity context, and relationship. Motivated by this observation, we propose a new holistic approach by 1) combining all similarity types used and 2) additionally considering relationship context similarity between pairs of named entities, a missing quadrant in the taxonomy of similarity metrics. We abstract the named entity translation problem as the matching of two named entity graphs extracted from the comparable corpora. Specifically, named entity graphs are first constructed from comparable corpora to extract relationship between named entities. Entity similarity and entity context similarity are then calculated from every pair of bilingual named entities. A reinforcing method is utilized to reflect relationship similarity and relationship context similarity between named entities. We also discover "latent" features lost in the graph extraction process and integrate this into our framework. According to our experimental results, our holistic graph-based approach and its enhancement using corpus latent features are highly effective and our framework significantly outperforms previous approaches.
Context, Feature extraction, Measurement, Dictionaries, Vectors, Data mining, Web sites, text mining, Data mining

M. Zhou, Y. Song, L. Jiang, J. Kim and S. Hwang, "Entity Translation Mining from Comparable Corpora: Combining Graph Mapping with Corpus Latent Features," in IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. , pp. 1787-1800, 2013.
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